import argparse
from pathlib import Path

import torch
from packaging import version
from torch.onnx import export

from diffusers import AutoencoderKL


A						=			version.parse(version.parse(torch.__version__).base_version) < version.parse('1.11')


def 	lowerCamelCase					(	UpperCamelCase     :				Optional[int]			,       UpperCamelCase     :				tuple			,       UpperCamelCase     :				Path			,       UpperCamelCase     :				str			,       UpperCamelCase     :				List[str]			,       UpperCamelCase     :				Tuple			,       UpperCamelCase     :				int			,       UpperCamelCase     :				List[Any]=False			,       )   ->    List[str]:
		output_path.parent.mkdir(parents=UpperCamelCase			,       exist_ok=UpperCamelCase							)
		# PyTorch deprecated the `enable_onnx_checker` and `use_external_data_format` arguments in v1.11,
		# so we check the torch version for backwards compatibility
		if is_torch_less_than_1_11:
				export(
				    UpperCamelCase			,       UpperCamelCase			,       f=output_path.as_posix()			,       input_names=UpperCamelCase			,       output_names=UpperCamelCase			,       dynamic_axes=UpperCamelCase			,       do_constant_folding=UpperCamelCase			,       use_external_data_format=UpperCamelCase			,       enable_onnx_checker=UpperCamelCase			,       opset_version=UpperCamelCase			,       )
		else:
				export(
				    UpperCamelCase			,       UpperCamelCase			,       f=output_path.as_posix()			,       input_names=UpperCamelCase			,       output_names=UpperCamelCase			,       dynamic_axes=UpperCamelCase			,       do_constant_folding=UpperCamelCase			,       opset_version=UpperCamelCase			,       )


@torch.no_grad()
def 	lowerCamelCase					(	UpperCamelCase     :				str			,       UpperCamelCase     :				str			,       UpperCamelCase     :				int			,       UpperCamelCase     :				bool = False							)   ->    Optional[Any]:
		_lowerCamelCase				   =  torch.floataa if fpaa else torch.floataa
		if fpaa and torch.cuda.is_available():
				_lowerCamelCase				   =  'cuda'
		elif fpaa and not torch.cuda.is_available():
				raise ValueError('`float16` model export is only supported on GPUs with CUDA'							)
		else:
				_lowerCamelCase				   =  'cpu'
		_lowerCamelCase				   =  Path(UpperCamelCase							)

		# VAE DECODER
		_lowerCamelCase				   =  AutoencoderKL.from_pretrained(model_path + '/vae'							)
		_lowerCamelCase				   =  vae_decoder.config.latent_channels
		# forward only through the decoder part
		_lowerCamelCase				   =  vae_decoder.decode
		onnx_export(
		    UpperCamelCase			,       model_args=(
		        torch.randn(1			,       UpperCamelCase			,       25			,       25							).to(device=UpperCamelCase			,       dtype=UpperCamelCase							),
		        False,
		    )			,       output_path=output_path / 'vae_decoder' / 'model.onnx'			,       ordered_input_names=['latent_sample', 'return_dict']			,       output_names=['sample']			,       dynamic_axes={
		        'latent_sample': {0: 'batch', 1: 'channels', 2: 'height', 3: 'width'},
		    }			,       opset=UpperCamelCase			,       )
		del vae_decoder


if __name__ == "__main__":
				A						=			argparse.ArgumentParser()

				parser.add_argument(
				    '--model_path',
				    type=str,
				    required=True,
				    help='Path to the `diffusers` checkpoint to convert (either a local directory or on the Hub).',
				)

				parser.add_argument('--output_path', type=str, required=True, help='Path to the output model.')
				parser.add_argument(
				    '--opset',
				    default=1_4,
				    type=int,
				    help='The version of the ONNX operator set to use.',
				)
				parser.add_argument('--fp16', action='store_true', default=False, help='Export the models in `float16` mode')

				A						=			parser.parse_args()
				print(args.output_path)
				convert_models(args.model_path, args.output_path, args.opset, args.fpaa)
				print('SD: Done: ONNX')
def 	lowerCamelCase					(	UpperCamelCase     :				int			,       UpperCamelCase     :				int							)   ->    str:
		if number < 0 or shift_amount < 0:
				raise ValueError('both inputs must be positive integers'							)

		_lowerCamelCase				   =  str(bin(UpperCamelCase							)							)
		binary_number += "0" * shift_amount
		return binary_number


def 	lowerCamelCase					(	UpperCamelCase     :				int			,       UpperCamelCase     :				int							)   ->    str:
		if number < 0 or shift_amount < 0:
				raise ValueError('both inputs must be positive integers'							)

		_lowerCamelCase				   =  str(bin(UpperCamelCase							)							)[2:]
		if shift_amount >= len(UpperCamelCase							):
				return "0b0"
		_lowerCamelCase				   =  binary_number[: len(UpperCamelCase							) - shift_amount]
		return "0b" + shifted_binary_number


def 	lowerCamelCase					(	UpperCamelCase     :				int			,       UpperCamelCase     :				int							)   ->    str:
		if number >= 0:  # Get binary representation of positive number
				_lowerCamelCase				   =  '0' + str(bin(UpperCamelCase							)							).strip('-'							)[2:]
		else:  # Get binary (2's complement) representation of negative number
				_lowerCamelCase				   =  len(bin(UpperCamelCase							)[3:]							)  # Find 2's complement of number
				_lowerCamelCase				   =  bin(abs(UpperCamelCase							) - (1 << binary_number_length)							)[3:]
				_lowerCamelCase				   =  (
				    '1' + '0' * (binary_number_length - len(UpperCamelCase							)) + binary_number
				)

		if shift_amount >= len(UpperCamelCase							):
				return "0b" + binary_number[0] * len(UpperCamelCase							)
		return (
		    "0b"
		    + binary_number[0] * shift_amount
		    + binary_number[: len(UpperCamelCase							) - shift_amount]
		)


if __name__ == "__main__":
				import doctest

				doctest.testmod()